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1.  Tractography reveals diffuse white matter abnormalities in Myotonic Dystrophy Type 1 
Cerebral involvement in Myotonic Dystrophy Type 1 (DM1) is well-established but not well characterized. This study applied new Diffusion Tensor Imaging (DTI) tractography to characterize white matter disturbance in adults with DM1. Forty-five participants with DM1 and 44 control participants had MRIs on a Siemens 3T TIM Trio scanner. Data were processed with TRActs Constrained by UnderLying Anatomy (TRACULA) and 7 tracts were evaluated. Bilateral disturbances in white matter integrity were seen in all tracts in participants with DM1 compared to controls. There were no right-left hemisphere differences. The resulting DTI metrics were correlated with cognitive functioning, particularly working memory and processing speed. Motor speed was not significantly correlated with white matter microstructural integrity and, thus, was not the core explanation for the working memory and processing speed findings. White matter integrity was correlated with important clinical variables including the muscular impairment rating scale (MIRS). CTG repeat length was moderately associated with white matter status in corticospinal tract and cingulum. Sleepiness (Epworth Sleepiness Scale) was moderately associated with white matter status in the superior longitudinal fasciculus and cingulum. Overall, the results add to an emerging literature showing widespread white matter disturbances in both early-onset and adult-onset DM1. Results suggest that further investigation of white matter pathology is warranted in DM1 and that non-invasive measures such as DTI have potentially important clinical value in characterizing the status of individuals with DM1.
doi:10.1016/j.jns.2014.04.005
PMCID: PMC4042407  PMID: 24768314
Myotonic Dystrophy; Brain; MRI; Diffusion Tensor Imaging; White Matter; Neuropsychology
2.  A Computational Linguistic Measure of Clustering Behavior on Semantic Verbal Fluency Task Predicts Risk of Future Dementia in the Nun Study 
Generative semantic verbal fluency (SVF) tests show early and disproportionate decline relative to other abilities in individuals developing Alzheimer’s disease. Optimal performance on SVF tests depends on the efficiency of using clustered organization of semantically related items and the ability to switch between clusters. Traditional approaches to clustering and switching have relied on manual determination of clusters. We evaluated a novel automated computational linguistic approach for quantifying clustering behavior. Our approach is based on Latent Semantic Analysis (LSA) for computing strength of semantic relatedness between pairs of words produced in response to SVF test. The mean size of semantic clusters (MCS) and semantic chains (MChS) are calculated based on pairwise relatedness values between words. We evaluated the predictive validity of these measures on a set of 239 participants in the Nun Study, a longitudinal study of aging. All were cognitively intact at baseline assessment, measured with the CERAD battery, and were followed in 18 month waves for up to 20 years. The onset of either dementia or memory impairment were used as outcomes in Cox proportional hazards models adjusted for age and education and censored at follow up waves 5 (6.3 years) and 13 (16.96 years). Higher MCS was associated with 38% reduction in dementia risk at wave 5 and 26% reduction at wave 13, but not with the onset of memory impairment. Higher (+1 SD) MChS was associated with 39% dementia risk reduction at wave 5 but not wave 13, and association with memory impairment was not significant. Higher traditional SVF scores were associated with 22–29% memory impairment and 35–40% dementia risk reduction. SVF scores were not correlated with either MCS or MChS. Our study suggests that an automated approach to measuring clustering behavior can be used to estimate dementia risk in cognitively normal individuals.
doi:10.1016/j.cortex.2013.05.009
PMCID: PMC4402214  PMID: 23845236
semantic verbal fluency; dementia; latent semantic analysis; clustering; Alzheimer’s disease
3.  Automated Semantic Indices Related to Cognitive Function and Rate of Cognitive Decline 
Neuropsychologia  2012;50(9):2165-2175.
The objective of our study is to introduce a fully automated, computational linguistic technique to quantify semantic relations between words generated on a standard semantic verbal fluency test and to determine its cognitive and clinical correlates. Cognitive differences between patients with Alzheimer’s disease and mild cognitive impairment are evident in their performance on the semantic verbal fluency test. In addition to the semantic verbal fluency test score, several other performance characteristics sensitive to disease status and predictive of future cognitive decline have been defined in terms of words generated from semantically related categories (clustering) and shifting between categories (switching). However, the traditional assessment of clustering and switching has been performed manually in a qualitative fashion resulting in subjective scoring with limited reproducibility and scalability. Our approach uses word definitions and hierarchical relations between the words in WordNet®, a large electronic lexical database, to quantify the degree of semantic similarity and relatedness between words. We investigated the novel semantic fluency indices of mean cumulative similarity and relatedness between all pairs of words regardless of their order, and mean sequential similarity and relatedness between pairs of adjacent words in a sample of patients with clinically diagnosed probable (n=55) or possible (n=27) Alzheimer’s disease or mild cognitive impairment (n=31). The semantic fluency indices differed significantly between the diagnostic groups, and were strongly associated with neuropsychological tests of executive function, as well as the rate of global cognitive decline. Our results suggest that word meanings and relations between words shared across individuals and computationally modeled via WordNet and large text corpora provide the necessary context to account for the variability in language-based behavior and relate it to cognitive dysfunction observed in mild cognitive impairment and Alzheimer’s disease.
doi:10.1016/j.neuropsychologia.2012.05.016
PMCID: PMC3404821  PMID: 22659109
semantic verbal fluency; Alzheimer’s disease; mild cognitive impairment; semantic similarity; semantic relatedness; computational semantics
4.  Ecology of aging human brain 
Archives of neurology  2011;68(8):1049-1056.
OBJECTIVE
Alzheimer’s disease (AD), cerebral vascular brain injury (VBI), and isocortical Lewy body (LB) disease (LBD) are the major contributors to dementia in community- or population-based studies: Adult Changes in Thought (ACT) study, Honolulu-Asia Aging Study (HAAS), Nun Study (NS), and Oregon Brain Aging Study (OBAS). However, the prevalence of clinically silent forms of these diseases in cognitively normal (CN) adults is less clear.
DESIGN and SETTING
We evaluated 1672 brain autopsies from ACT, HAAS, NS, and OBAS of which 424 met criteria for CN.
MAIN OUTCOME MEASURES
Of these, 336 cases had a comprehensive neuropathologic examination of neuritic plaque (NP) density, Braak stage for neurofibrillary tangles (NFTs), Lewy body (LB) distribution, and number of cerebral microinfarcts (CMIs).
RESULTS
47% of CN cases had moderate or frequent NP density; of these 6% also had Braak stage V or VI for NFTs. 15% of CN cases had medullary LBD; 8% also had nigral and 4% isocortical LBD. The presence of any CMIs was identified in 33% and high level CMIs in 10% of CN individuals. Overall burden of lesions in each individual and their co-morbidity varied widely within each study but were similar among studies.
CONCLUSIONS
These data show an individually varying complex convergence of subclinical diseases in the brain of older CN adults. Appreciating this ecology should help guide future biomarker or neuroimaging studies as well as clinical trials that focus on community- or population-based cohorts.
doi:10.1001/archneurol.2011.157
PMCID: PMC3218566  PMID: 21825242
Alzheimer’s disease; vascular brain injury; Lewy body disease; cognitive aging

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